Deep Learning

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Deep Learning

Deep Learning comes under machine learning and it is based on the neural network architecture. In case of Deep Learning system learns from the large data sets automatically.

Deep Learning comes under machine learning and it is based on the neural network architecture. In case of Deep Learning system learns from the large data sets automatically.

Deep Learning - Tutorials, articles, examples, case studies and news

The Deep Learning is based on the way human biological neural network works and it is an automated way of learning from large collection of data. The Deep Learning is subset of machine learning and this is also known as known as deep structured learning or hierarchical learning. Deep learning is based on artificial neural networks and it can be used for supervised, semi-supervised or unsupervised learning. But in most of the cases it is being used as highly efficient supervised way of learning from vast collection of data. The accuracy of predictions is very high in case of model models developed with deep learning.

Currently deep learning is used many fields like image, video, text and audio processing. Its also successfully used in computer vision, object detection, voice recognition, language translation and speech translation from one language to another language. There is unlimited use of deep learning technologies in today's world for example self-driving car is also using model developed in deep learning technologies. The most import industries deep learning is used are Self-Driving Cars, Fake News detection, NLP, Virtual Assistance, Film making, Animation, Visual recognition, Fraud detection, health care and many other domains.

Deep Learning

The deep learning is based on the neural networks architectures such as deep neural networks, deep belief networks, recurrent neural networks and Convolutional neural networks. One or combinations of one or more architectures are used in the various fields such as:

  • Computer vision
  • Speech recognition
  • Natural language processing
  • Audio recognition
  • Social network security analysis and filtering of data
  • The machine translation of text
  • Bioinformatics
  • Drug discovery and design
  • Analysis of medical images
  • Gaming applications
  • Banking and finance domain

The deep learning technologies are used where there is need to develop human like intelligence into the system. Deep learning technologies are very helpful in developing such a high intelligent system that can match human intelligence. The development of such system requires huge data sets and proper training of deep neural networks. Today's deep learning technologies such as TensorFlow 2.0 comes with the libraries are tools for training highly intelligent deep neural network models.

The deep learning is based on the Artificial Neural Networks or ANNs for short. The ANN is designed after studying the information processing and communications of biological neuron networks. The design of Artificial Neural Networks are inspired by biological neural networks and it try to achieve human like intelligence.

These days deep learning models are mostly based on the artificial neural networks and most used neural network are Convolutional Neural Networks (CNN)s. In deep learning other types of neural networks and various mathematical functions are also used to make model perform well. Data scientists also use deep belief networks and deep Boltzmann machines to develop various layers of the neural networks.

The design of neural network is mostly depends on the type of problem we are going to solve. So, the field of deep learning is very large and it requires knowledge of many fields of science. For learning deep learning one should have through understanding of data processing, machine learning, Mathematics (Statistics, Probability, Vectors etc..), programming languages and machine learning libraries.

Data scientist also should about the Big Data technologies, databases and data visualization technologies.

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